Article
Short-Term Belowground Responses to Thinning and
Burning Treatments in Southwestern Ponderosa Pine
Forests of the USA
Steven T. Overby 1,2, *,† and Stephen C. Hart 2,3,†
1
2
3
*
†
Rocky Mountain Research Station, USDA Forest Service, Flagstaff, AZ 86001, USA
School of Forestry, Northern Arizona University, Flagstaff, AZ 86011, USA; shart4@ucmerced.edu
Life and Environmental Sciences, Sierra Nevada Research Institute, University of California, Merced,
CA 95343, USA
Correspondence: soverby@fs.fed.us; Tel.: +1-928-556-2184; Fax: +1-928-556-2130
These authors contributed equally to this work.
Academic Editors: Dale Johnson and Eric J. Jokela
Received: 8 September 2015; Accepted: 1 February 2016; Published: 18 February 2016
Abstract: Microbial-mediated decomposition and nutrient mineralization are major drivers of forest
productivity. As landscape-scale fuel reduction treatments are being implemented throughout
the fire-prone western United States of America, it is important to evaluate operationally how
these wildfire mitigation treatments alter belowground processes. We quantified these important
belowground components before and after management-applied fuel treatments of thinning alone,
thinning combined with prescribed fire, and prescribed fire in ponderosa pine (Pinus ponderosa) stands
at the Southwest Plateau, Fire and Fire Surrogate site, Arizona. Fuel treatments did not alter pH, total
carbon and nitrogen (N) concentrations, or base cations of the forest floor (O horizon) or mineral
soil (0–5 cm) during this 2-year study. In situ rates of net N mineralization and nitrification in the
surface mineral soil (0–15 cm) increased 6 months after thinning with prescribed fire treatments;
thinning only resulted in net N immobilization. The rates returned to pre-treatment levels after one
year. Based on phospholipid fatty acid composition, microbial communities in treated areas were
similar to untreated areas (control) in the surface organic horizon and mineral soil (0–5 cm) after
treatments. Soil potential enzyme activities were not significantly altered by any of the three fuel
treatments. Our results suggest that a variety of one-time alternative fuel treatments can reduce fire
hazard without degrading soil fertility.
Keywords: fuel treatments; nitrification; nitrogen mineralization; phospholipid fatty acids;
soil enzymes
1. Introduction
When Euro-Americans settled in the southwestern United States of America (USA), the ponderosa
pine (Pinus ponderosa P. & C. Lawson) forests they encountered were more open landscapes than
today, with clusters of pine trees interspersed in meadows dominated by grasses [1,2]. Frequent fires
(every 2–20 year), herbaceous competition, and periodic drought maintained the pre-Euro-American
forest structure with densities of 30–140 trees ha´1 [3,4]. Following Euro-American settlement, fire
exclusion, livestock grazing, and removal of large pre-settlement trees increased stand densities
dramatically [5–7]. Removal of the dominant trees, coupled with a large seedling recruitment of
ponderosa pine in 1919 [8,9], increased stand densities to the current 727 trees ha´1 on average in
Arizona [5], with some stands exceeding 2000 trees ha´1 [6,7]. As tree density increased, canopies
Forests 2016, 7, 45; doi:10.3390/f7020045
www.mdpi.com/journal/forests
Forests 2016, 7, 45
2 of 19
became more continuous and understory vegetation declined. High-severity wildfires in dry, fire-prone
forests of the southwestern United States (US) have increased in frequency and size after a century
or more of increasing tree density and accumulation of fuels [10,11]. Reducing stand densities and
fuels in these forests have been shown to decrease fire severity [12,13], but with several alternative fuel
reduction strategies available [14,15] the question becomes: What strategy best serves the resilience of
these fire-adapted forests?
Prescribed fire has been used to reduce ladder fuels that increase the likelihood of crown fires,
but also to control pest and diseases [16]. Various levels of thinning to reduce tree density, frequently
combined with prescribed fire, have also been proposed to reduce fuel quantities and continuity [16].
Mitigating wildfire behavior currently is based on limited observational data from wildfires that have
moved into previously treated stands [16] and fire behavior modeling [17]. Our knowledge of the
ecological effects of these treatments is lacking and a major concern for land managers.
Fire can reduce detrital inputs to soil and result in the loss of decomposer microorganisms due
to lethal temperatures [18]. Soil bacteria and fungi, the primary decomposers, process between 80%
and 90% of all plant detritus via production of extracellular enzymes [19]. Even though losses of
organic matter due to combustion can be as high as 85%, increased nitrogen (N) availability [20–23],
soil insolation and soil moisture [23,24], surface soil pH (cation deposition), and the addition of
charcoal [23] can enhance microbial activity [25,26]. Yet prescribed fire can also negatively impact
soils by forming hydrophobic surface conditions limiting water infiltration and available soil moisture,
reducing microbial activity [18], and disproportionately decreasing fungal biomass especially in the
surficial organic (O) horizon [27].
Soil microbial composition is also influenced following thinning by changes in the soil
microclimate, such as increased soil moisture and soil temperatures, and levels of harvesting residue
left on site [28,29]. In some short-term studies, decomposition and N mineralization rates have been
shown to increase in southwestern ponderosa pine [21,30,31]. These increases in soil process rates
were hypothesized to result from increased soil microbial activity in the warmer and wetter surface
soil following partial canopy removal [21,30]. However, net N immobilization can also occur with
increased soil microbial activity [32].
An assessment of the effects of fuel reduction treatments on forest soils has been challenging
due to idiosyncratic results from fire research [33], and vastly different application of mechanical
treatments [14]. The Fire and Fire Surrogate (FFS) network study was developed specifically to
evaluate short- and long-term ecological effects following alternative fuel reduction treatments [34].
These fuel reduction treatments were applied to fire-prone forests at 12 sites across US, with eight
replicates in ponderosa pine-dominated ecosystems. A common attribute among the 12 sites is the
change from frequent low- to moderate-severity fires to infrequent high-severity fires that have a
potential for catastrophic consequences [34]. The requirement for the FFS treatments were to reduce
stand and fuel conditions such that, if impacted by a head fire under 80th percentile weather conditions,
at least 80 percent of the basal area of dominant and codominant trees would survive [34,35]. The
treatment methods primary purpose was to modify fire behavior by reducing quantity and continuity
of forest fuels [36]. Prescribed fire has been a common management practice, yet greater restraints
have been placed on utilization of prescribed fire due to increased social and administrative issues
with increasing populations living in closer proximity to these forests [37]. Surrogate methods, such
as thinning and thinning combined with fire, were used at all of the FFS study sites to provide
similar reductions in fuel loading as prescribed fire alone, and an experimental design with consistent
treatments and measured variables at a landscape scale [35].
The objectives of our study were to quantify the short-term (2 years) effects of FFS fuel treatments
on soil chemical properties, and microbial activity and composition at the Southwest Plateau (SWP) site
of the FFS study. We measured all of the annual FFS network response variables; in addition, we added
a shorter term sampling period (6 months) and included a more thorough microbial assessment
through the use of phospholipid fatty acid (PLFA) measurements and additional soil enzyme potential
Forests 2016, 7, 45
3 of 19
activities. We hypothesized: (1) Prescribed fire will reduce forest floor (O horizon) quantity in the
short-term, but increase quality immediately post-treatment (for microbial decomposition and nutrient
mineralization), increasing microbial activity. A decrease in microbial populations due to mortality
will be minimal given the low severity of prescribed fires, but more pronounced in the forest floor
compared to the mineral soil. (2) The thinning only treatment will add organic material to the forest
floor and mineral soil surface, which will increase microbial activity but immobilize N due to the low
quality of the added organic inputs (e.g., woody materials). And (3) thinning followed by prescribed
fire will increase detrital organic matter and nutrient inputs to the soil, but will also increase nutrient
availability of the residual organic matter due to oxidation from burning.
2. Experimental Section
2.1. Study Site and Experimental Design
The ponderosa pine forests of the southwestern USA are the driest and most variable with respect
to annual precipitation of all the FFS locations. The SWP site, located in north-central Arizona on
the Coconino Plateau, was one of the eight ponderosa pine ecosystems within the FFS network. The
SWP site is located in northern Arizona and consists of a randomized, complete block experimental
design with 3 blocks and 4 treatments. Two of the replicate blocks (Rudd Tank: 35˝ 14.0’05.9”,
111˝ 44.0’58.4”, and Powerline: 35˝ 12.0’33.9”, 111˝ 45.0’32.2”) are located on the Coconino National
Forest west of Flagstaff, Arizona, and a third block (KA Hill: 35˝ 12.0’33.9”, 111˝ 45.0’32.2”) on the
Kaibab National Forest southeast of Williams, Arizona (Figure 1). Elevations range from 2217 to 2264 m
and the forest overstory is dominated by ponderosa pine, with small populations of alligator juniper
(Juniperus deppeana Steud.) and Gambel oak (Quercus gambelii Nutt.; [38]).
Soils at Powerline and Rudd Tank areas are dominated by the Typic Eutroboralfs soil
subgroup [39], while KA Hill area is primarily Lithic Eutroboralfs [40]. Soil family designations
are either fine, smectitic or clayey-skeletal, smectitic, with 35% to 60% clay in the fine earth fraction
(<2 mm). Mean annual precipitation is 547 mm with a bimodal distribution. Approximately half of the
precipitation falls as snow (mean annual amount = 210 cm) and the other half as late summer rains.
Little precipitation occurs from May to early July, resulting in low soil moisture content until the onset
of summer rains in mid-July to September. This summer rainy period is followed by another dry period
prior to winter precipitation. Long-term mean annual air temperature is 7.5 ˝ C, with annual maximums
of 15.7 ˝ C and annual minimums of ´3.4 ˝ C. During our study, the pre-treatment year (2001) had
a total precipitation of 401 mm with 29 mm coming as snow, while the post-treatment years (2004,
2005) total precipitation was 694 mm and 735 mm respectively, with 137 mm and 153 mm of snowfall.
Air temperatures were much less variable. Mean daily maximum air temperature in year 2001 was
17.8 ˝ C, while the 2004 and 2005 years were 17.4 ˝ C and 17.2 ˝ C; mean daily minimum temperatures
were 3.4, 3.4, and 3.6 ˝ C for 2001, 2004, and 2005, respectively. These values were obtained from
the Western Regional Climate Center (http://www.wrcc.dri.edu/summary/climsmaz.html) for the
Ft. Valley Station (029359), which is approximately the same elevation as the study sites and 5 km
northeast of the Coconino National Forest sites and 25 km northeast of the Kaibab National Forest site.
Treatment unit boundaries followed existing stand and natural landscape features that contained
a core 10-ha sampling area. Treatments varied in size from 14 to 16 ha, with at least a 50-m buffer
between adjacent units (Figure 1). Permanent boundaries and sampling point centers were established
prior to treatments. Thinning treatments began in late 2002 and were completed in the spring of 2003.
Thinning was accomplished by hand felling trees, then limbing and sawing into shorter lengths in
place. Logs were skidded by rubber-tired vehicle to landings, and then loaded onto trucks. Treatment
goals were to reduce stem density to 116 trees ha´1 with a residual overstory of 12–14 m2 ¨ ha´1 ,
creating an uneven-aged forest structure [38]. Fire only treatments did not meet either target. Neither
thinning only nor thinning plus fire treatments reduced tree density to target levels, while thinning
plus fire treatments met the basal area goal and thinning only treatments were just slightly higher than
The ponderosa pine forests of the southwestern USA are the driest and most variable with respect to annual precipitation of all the FFS locations. The SWP site, located in north‐central Arizona on the Coconino Plateau, was one of the eight ponderosa pine ecosystems within the FFS network. The SWP site is located in northern Arizona and consists of a randomized, complete block Forests 2016, 7, 45
4 of 19
experimental design with 3 blocks and 4 treatments. Two of the replicate blocks (Rudd Tank: 35°14.0′05.9”, 111°44.0′58.4”, and Powerline: 35°12.0′33.9”, 111°45.0′32.2”) are located on the Coconino National Forest west of Flagstaff, Arizona, and a third block (KA Hill: 35°12.0′33.9”, 111°45.0′32.2”) the basal area target. Thinning only and thinning plus fire treatments were based on an uneven-age
on the Kaibab National Forest southeast of Williams, Arizona (Figure 1). Elevations range from 2217 group selection design, with the majority of trees harvested falling into diameters (at breast height;
to m and between
the forest overstory dominated by ponderosa pine, with small of 1.42264 m) classes
5 and
35 cm. is Additional
details
on vegetation
structure
and populations fuels of the FFS
alligator juniper (Juniperus deppeana Steud.) and Gambel oak (Quercus gambelii Nutt.; [38]). study sites can be found in Schwilk et al. [41] and Youngblood [42].
Rudd Tank FFS site
Powerline FFS site
KA Hill FS site
Figure 1. Southwest Plateau Fire and Fire Surrogate block locations and a generalized treatment plot Figure 1. Southwest Plateau Fire and Fire Surrogate block locations and a generalized treatment plot
design. Each of four 10‐ha core treatment units are within each replicate block. Each treatment unit design. Each of four 10-ha core treatment units are within each replicate block. Each treatment unit
has 36 (typically 6 ˆ 6) permanent gridpoints located 50 m apart oriented north to south. The small
squares shown within each treatment unit are soil3subplots (20 ˆ 50 m) located adjacent to every other
permanent gridpoint for up to ten soil subplots. Each soil subplot’s southeast corner is 10 m east of
each permanent gridpoint.
Prescribed fire treatments followed in the fall of 2003 [38]. Fire only and thinning plus fire units
were broadcast burned at the same time as the slash piles were burned. Slash from thinning only
treatments was removed offsite, while the thinning plus fire slash was piled in buffer zones outside
of the experimental area then burned. Approximately 3.0 Mg ha´1 of slash residue remained within
the treatment areas. The fire treatments were completed over a one-month period from September 30
to October 22, 2003. Burning conditions ranged between 6.4 and 19.3 km per hour for wind speeds,
21.7 and 27.2 ˝ C for maximum air temperatures, and 9% and 29% for relative humidity. Flame lengths
varied at the thinning plus fire treatments between 23 and 51 cm, and 38 and 51 cm for the fire only
treatments. Treatments were performed by commercial contractors for thinning and National Forest
personnel for prescribed fires. Fire only treatments reduced forest floor biomass by 53%, whereas
thinning plus fire treatments reduced forest floor biomass by 37% and thinning-only by 6% (Table S1).
Forests 2016, 7, 45
5 of 19
2.2. Soil Sampling and Nutrient Analyses
We followed the experimental design measuring the core soil variables detailed in the FFS
study, and augmented these measurements by including phospholipid fatty acids (PLFA) to quantify
possible compositional changes to the soil microbial community following the FFS treatments. We also
measured additional soil potential enzyme activities important for organic matter decomposition and
nutrient cycling. Finally, we included a 6-month post-treatment sampling, in addition to the post one
and two year measurements implemented at all the sites, to evaluate the more immediate impact of
the treatments on N cycling.
Within each 10-ha core treatment unit, 10 sub-plots (20-m ˆ 50-m) were established for soil
sampling. Each treatment unit has 36 (typically 6 ˆ 6) permanent gridpoints located 50 m apart
oriented north to south. Each soil subplot’s southeast corner is 10 m east of every other permanent
gridpoint for up to ten soil subplots. Pre-treatment sampling (September 2001) was performed at each
soil sub-plot [10] for all treatments [4] within each replicate block [3]. The forest floor was collected at
randomly selected points within a 0.01-m2 litter frame, placed in polyethylene bags, and transported
on ice to the laboratory. Mineral soil (0–5 cm) was also sampled within the litter frame after removing
the forest floor, using a 2.22-cm diameter slotted soil probe (AMS, American Falls, ID 83211). Upon
arrival at the laboratory, we discarded all material greater than 6-mm diameter for the forest floor
samples and sieved the mineral soil samples (< 2 mm). All samples were weighed, and a subsample
(20 g) was taken from both the forest floor and mineral soil. Each subsample was placed in a drying
oven for 48 h (forest floor 70 ˝ C, mineral soil 105 ˝ C), then reweighed to determine water content.
Additional subsamples were taken from the forest floor and mineral soil for soil microbial analyses
(10 g), along with mineral soil subsamples for enzyme (5 g) and community-level physiological profile
(CLPP; 5 g) analyses. Enzyme assays and CLPP analyses samples were stored no longer than 12 h
at 4 ˝ C. The remaining portion of each sample was then air-dried. The forest floor was analyzed for
total C and N concentrations, and mineral soil was analyzed for pH, total C and N concentrations,
mineral soil extractable base cations, and potential enzyme activities. Air-dried, well-mixed forest
floor samples and mineral soil were individually ground until the entire sample passed through
a No. 100 sieve (<0.149 mm). Subsamples (20–50 mg) of these materials were then analyzed for
total C and N concentrations on an elemental analyzer (Flash EA 1112, CE Elantech, Lakewood, NJ,
USA). Extractable cations (calcium (Ca), magnesium (Mg), potassium (K), sodium (Na), iron (Fe),
and aluminum (Al)) of sieved, air-dried mineral soil were determined using the method described
by Hendershot et al. [43], with elemental concentrations measured with a flame atomic absorption
spectrophotometer (Perkin-Elmer AAnalyst 100, Waltham, MA, USA). Briefly, cations were extracted
using 30 mL of 0.1 M BaCl2 from a 1 g subsample. Cation exchange capacity (CEC) was calculated by
summing the extractable base cations found in our soil samples. Soil pH was determined by immersing
a glass electrode into a 1:5 (w/v) soil-to-0.01 M CaCl2 solution [43] connected to a Orion 550A pH
meter (Thermo Fisher Scientific, Inc., Waltham, MA, USA).
Soil samples were again collected and analyzed at three post-treatment periods following
pre-treatment protocols except where noted below. Post-treatment samples were taken 6 months
post-treatment (April 2004), 1-year post-treatment (October 2004), and 2-year post-treatment
(September 2005). Mineral soil extractable base cations were measured on pre-treatment and 1-year
post-treatment mineral soil only, while PLFA analyses were conducted on 1-year post-treatment forest
floor and mineral soil only due to funding constraints. For these analyses, three replicate subsamples
were composited from three adjacent sub-plot samples within each treatment unit for both forest floor
and mineral soil.
2.3. Microbial Community Analyses
Forest floor and mineral soil composites were assessed for microbial biomass and structure. These
subsamples were frozen for 24 h, then freeze-dried (´50 ˝ C, 7 ˆ 10´3 kPa for 24 h, Edwards Modulyo,
Crawley, UK) prior to extraction for PLFA. The extraction process occurred within 48 h of returning
Forests 2016, 7, 45
6 of 19
to the laboratory. Compounds between C14 to C18 in C chain length used as microbial biomarkers
were identified using mass spectrometry. We used biomarkers: i15:0, a15:0, i16:0, i17:0, and a17:0 for
Gram-positive bacteria [44]; cy17:0, cy19:0, 16:1w9c, 16:1w7, 18:1w5c, and 18:1w7 for Gram-negative
bacteria [44,45]; 18:2w6,9 for fungi [44,46]; and 10me16:0 to represent Actinobacteria [47]. Five g of
freeze-dried mineral soil sieved (<2 mm) or 2 g of freeze-dried forest floor (ground to <0.149 mm)
were extracted with a single-phase mixture of chloroform, methanol, and phosphate buffer [48],
followed by fractionation into neutral, glyco-, and phospholipids [49]. We followed the extraction
and analysis method described in Schweitzer et al. [50]. Quantification (mmol PLFA kg´1 oven-dry
material) of samples was based on calibration curves derived from individual Fatty Acid Methyl Esters
(FAME) standards.
2.4. Microbial Activity and Net N Transformations
To determine if FFS treatments affected microbial activity and function, the potential
activities of eight ecologically relevant enzymes were assayed: β-1,4-glucosidase, α-1,4-glucosidase,
β-galactosidase, β-xylosidase, cellobiohydrolase, N-acetyl-glucosaminidase, alkaline phosphatase,
and sulfatase. These eight enzymes were measured using the methylumbelliferone (MUB)-linked
substrates [51]. The first five enzymes decompose carbohydrates and polysaccharides into
energy sources accessible by soil organisms [52–54]. N-acetyl-glucosaminidase contributes to the
mineralization of N from chitin [55], phosphatase releases inorganic P by breaking ester linkages [51],
and sulfatase breaks ester linkages releasing inorganic forms of sulfur [54,56]. Methods for enzyme
assays followed those outlined by Boyle et al. [50]. Field-moist soil (1 g) was first suspended in 100 mL
of 5 mM bicarbonate buffer solution (pH 8.2), then an aliquot (100 µL) of this soil solution was added
with 100 µL of an enzyme substrate solution to a single microtiter plate well. All eight enzyme
substrates followed this procedure six times with quenching standards included on each plate [57].
Plates were immediately read using a Fluoromax fluorometer (Jobin Yvon-Spex, Edison, NJ, USA) with
an attached MicroMax Microwell plate reader (excitation of 360 nm, emission 450 nm). Plate incubation
was 1 h at 27 ˝ C before the final fluorometric reading.
Community-level physiological profiles were also used for determining potential differences in
microbial activity among fuel treatments. Community-level physiological profiles provide a means
of assessing soil microbial communities based on carbon (C) substrate utilization [58]. They are
indicative of the metabolic potential of the microbial community [59], and provide a qualitative
measure of functional diversity [60]. We made use of microtiter plates (Biolog™, Inc, Hayward, CA,
USA) developed for both bacteria (Biolog™ EcoPlate) and fungi (Biolog™ SFN2). Bacterial microtiter
plates contain 31 different C substrates replicated three times on each plate, while fungal microtiter
plates contain 95 individual C substrates. A tetrazolium dye sensitive to reduction is included with
each C substrate on bacterial microtiter plates. Fungal microtiter plates do not include the tetrazolium
dye due to toxicity to some fungi [61]. This dye develops a purple color if catabolized, while fungal
microtiter plates utilize turbidity as a measurement. We followed the CLPP method detailed in
Classen et al. [58].
We used the in situ covered-core method [62] to estimate the potential impact of fuel reduction
treatments on net N mineralization and nitrification rates in the upper 15 cm of mineral soil. At the
same time and adjacent (<1 m away) to locations where samples were taken for the other assays,
two intact soil cores (0–15 cm) were removed using a corer attached to a slide hammer (AMS, Inc.,
American Falls, ID, USA) containing a 5-cm ˆ 15-cm thin-walled polycarbonate inner sleeve. A plastic
cap with six small holes (<1 mm dia.) was placed over the top of the core and this core was then
returned to its original location and forest floor placed over the mineral soil core. The purpose of
these small holes was to improve gas exchange between the incubated soil contained within the
polycarbonate sleeve and the surrounding environment, while minimizing water loss or gain by the
confined soil. The second core was covered at both ends with solid plastic caps and transported to the
laboratory on ice. Immediately after returning to the laboratory, the soil within these initial cores was
Forests 2016, 7, 45
7 of 19
sieved (<4 mm), mixed, weighed, and subsampled (15 g field-moist mass) to determine gravimetric
water content and soil inorganic N pools.
Approximately 5 g (field moist) of soil were extracted with 50 mL of 2 M KCl. Soil suspensions
were shaken for 30 min. on a reciprocating shaker, and then filtered through pre-leached (with
deionized water), Whatman No. 1 filter paper; filtered aliquots were frozen until analyzed [62].
Ammonium and NO3 ´ concentrations were determined colorimetrically from the KCl extracts using
a Flow Injection Analyzer [63,64]. After 28 days, the in situ incubated cores were removed and
processed in the same manner as these initial cores. Soil net N transformation rates were determined
pre-treatment, and 6 months, 1 year, and 2-year post-treatment. Net N mineralization over the
incubation period was calculated by subtracting the initial inorganic N pools (NH4 + -N + NO3 ´ -N) from
the final post-incubation N pools. Net nitrification was calculated similarly using only the NO3 ´ -N
pool. Subplot mean soil bulk density values (Mg¨m´3 ) of the <4-mm fraction were determined from
initial and incubated cores for each subplot across all sampling periods and used to convert mass-based
net N transformation rates to an areal basis for that subplot. All mass-based soil values are expressed
on an oven-dry mass basis (70 ˝ C for forest floor, 105 ˝ C for mineral soil).
2.5. Statistical methods
The FFS experimental design is a randomized complete block design. Each treatment plus a control
was randomly assigned within each block, with three replicate blocks (Figure 1). Measurement
of response variables was performed prior to treatment and post-treatment. We used a repeated
measures generalized linear mixed model to analyze univariate responses of forest floor mass, total
mineral soil C and N concentrations, pH, enzyme activity, extractable cations, CEC, and soil net
N transformations (GLIMIXX, SAS for PCs ver. 9.4, Copyright © 2012, SAS Institute Inc., Cary,
NC, USA) to determine differences based on treatment, sampling date, and treatment ˆ sampling
date interactions. Natural logarithmic transformations of data were used in all statistical analyses
except for pH due to heteroscedacity of residuals in the model. We designated block as a random
effect and used sampling date (categorical time interval) as the repeated measure. By designating
block as a random effect instead of a fixed effect, the treatment error estimate does not include
variability attributable to block. Within the GLIMIXX procedure, we designated a normal probability
distribution with an identity link function. This procedure tests for differences in treatments on
response variables adjusted for any pre-treatment site spatial dependence to the response variable [65].
The temporal correlation is nested within each site and treatment combination. Each subject is
a site ˆ thin ˆ burn combination (thinning only 0 or 1, fire only 0 or 1, and thinning plus fire 1
and 1) such that the temporal correlation within each site is modeled and not pooled across sites.
An unstructured variance-covariance matrix is fit to account for inter-year correlation in each treatment
ˆ block combination. The variances are constrained to be non-negative, while the covariance matrix
of the fixed-effect parameter estimates (treatments) are unconstrained in order to avoid non-linear
constraints. Denominator degrees of freedom for t and F tests were determined using Kenward
and Roger approximation [66]. Generalized linear mixed models in ecological studies allow greater
generalizations of conclusions by incorporating all random effects into traditional blocked-design
experiments [67].
Multivariate responses of PLFA biomarkers and CLPPs for each sample were analyzed using
multi-response permutation procedure (MRPP) [68]. Phospholipid fatty acid data were normalized
to express the proportion of each specific biomarker mass relative to the total mass of all biomarkers
for a given sample [69]. Community-level physiological data was normalized similarly using
absorbance values. Phospholipid fatty acid biomarkers grouped by fungi, gram-positive bacteria, and
gram-negative bacteria, and CLPP data grouped by substrate guild, were also analyzed statistically
using GLIMIXX procedure described above. Multi-response permutation procedure does not require
assumptions of multivariate normality or homogeneity of variances, which are seldom met with
ecological community data [69]. Simultaneous pairwise comparisons, using the Peritz closure method
Forests 2016, 7, 45
8 of 19
to maintain Type I error rate and a prior alpha level (α = 0.05), tested the null hypothesis that all possible
pairs are similar [70]. This procedure was performed using Microsoft Excel macros (available from
the corresponding author) following the methodology of Mielke and Berry [68]. All values presented
Forests 2016, 7, 45 within the text are arithmetic means and standard errors (i.e., not back-transformed) across the three
sites (n = 3) by treatment block. Site values for each treatment block were calculated from the mean
the three sites (n = 3) by treatment block. Site values for each treatment block were calculated from values across the subplots (n = 10) within each block at that site.
the mean values across the subplots (n = 10) within each block at that site. 3. Results
3. Results For the variables we analyzed pre-treatment, there were no significant differences within blocks
For the variables we analyzed pre‐treatment, there were no significant differences within blocks among the assigned treatment units. Additionally, post-treatment measurements of total C and N
among the assigned treatment units. Additionally, post‐treatment measurements of total C and N concentrations (%) and carbon to nitrogen (C:N) mass ratios of the O horizon were also not significantly
concentrations (%) and carbon to nitrogen (C:N) mass ratios of the O horizon were also not different (Table S2). There was a significant reduction in forest floor mass for fire ˆ time interaction
significantly different (Table S2). There was a significant reduction in forest floor mass for fire × time (p = 0.043, denominator degrees of freedom (den. df ) = 6). Total C concentration was significantly higher
interaction (p = 0.043, denominator degrees of freedom (den. df) = 6). Total C concentration was for treatments that included thinning post- treatment for the mineral soil (p = 0.025, den. df = 7.988),
significantly higher for treatments that included thinning post‐ treatment for the mineral soil (p = while total N concentration, C:N, or pH were not significantly different for main effect or treatment by
0.025, den. df = 7.988), while total N concentration, C:N, or pH were not significantly different for main time interactions. Analysis of extractable cations (Ca, Mg, K, Na) and CEC also were non-significant
effect or treatment by time interactions. Analysis of extractable cations (Ca, Mg, K, Na) and CEC also (Table S3). Both Fe and Al were below our detection limits (0.1 mg Fe L´1 , 0.03 mg Al L´1 ). There also
were non‐significant (Table S3). Both Fe and Al were below our detection limits (0.1 mg Fe L−1, 0.03 were several significant sampling date differences due to seasonal and interannual variations for the
mg Al L−1). There also were several significant sampling date differences due to seasonal and forest floor (total N concentration p = 0.036, den. df = 5.914; C:N ratio p = 0.002, den. df = 6.309) and
interannual variations for the forest floor (total N concentration p = 0.036, den. df = 5.914; C:N ratio p = 0.002, mineral soil (pH p = 0.028, den. df = 6; total N concentration p = 0.028, den. df = 6), but no significant
den. df = 6.309) and mineral soil (pH p = 0.028, den. df = 6; total N concentration p = 0.028, den. df = 6), treatment by time interaction.
but no significant treatment by time interaction. Total microbial biomass based on PLFA was not statistically different among treatments after
Total microbial biomass based on PLFA was not statistically different among treatments after 1‐
1-year (forest floor p = 0.185, den. df = 8.155; mineral soil p = 0.662, den. df = 8.155), yet PLFA biomarkers
year (forest floor p = 0.185, den. df = 8.155; mineral soil p = 0.662, den. df = 8.155), yet PLFA biomarkers by groups (i.e., fungi, gram-positive bacteria, and gram-negative bacteria) for forest floor did indicate
by groups (i.e., fungi, gram‐positive bacteria, and gram‐negative bacteria) for forest floor did indicate a treatment difference when fire was part of the treatment using GLIMMIX analysis (forest floor
a treatment difference when fire was part of the treatment using GLIMMIX analysis (forest floor p = 0.012, p = 0.012, mineral soil p = 0.163, den. df = 8.155; Figure 2). Fungi to bacteria ratios showed no treatment
mineral soil p = 0.163, den. df = 8.155; Figure 2). Fungi to bacteria ratios showed no treatment differences after 1-year post-treatment (forest floor p = 0.229, den. df = 8.155; mineral soil p = 0.163,
differences after 1‐year post‐treatment (forest floor p = 0.229, den. df = 8.155; mineral soil p = 0.163, den. den. df = 8.155).
df = 8.155). 250
A
Control
Fire only
Thinning only
Thinning plus fire
a
200
mol PLFA . kg-1 O-horizon
a
150
100
b
b
50
0
Figure 2. Cont.
Forests 2016, 7, 45
Forests 2016, 7, 45 9 of 19
25
B
15
-1
mol PLFA kg soil
20
10
5
0
Fungi
Gram-positive
bacteria
Gram-negative
bacteria
Figure 2. Mean microbial biomass in the forest floor (A) and mineral soil (0–5 cm; B) sampled 1-year
Figure 2. Mean microbial biomass in the forest floor (A) and mineral soil (0–5 cm; B) sampled 1‐year post fuel reduction treatments at the Southwestern Plateau, Fire and Fire Surrogate study. Microbial
post fuel reduction treatments at the Southwestern Plateau, Fire and Fire Surrogate study. Microbial biomass of different functional groups (mean + SE; n = 3) was determined using phospholipid fatty acid
biomass of different functional groups (mean + SE; n = 3) was determined using phospholipid fatty analysis (PLFA). Different letters for a given sampling date indicate significant differences (α = 0.05)
acid analysis (PLFA). Different letters for a given sampling date indicate significant differences (α = using using Tukey-Kramer
pairwise
comparison,
generalized
linear mixed
model
analysis
treatments.
0.05) Tukey‐Kramer pairwise comparison, generalized linear mixed model ofanalysis of When
no
letters
are
provided,
mean
values
among
treatments
were
similar
statistically.
treatments. When no letters are provided, mean values among treatments were similar statistically. Potential enzyme activity in mineral soil (0–5 cm) for pre‐treatment sampled in the fall of 2001 Potential enzyme activity in mineral soil (0–5 cm) for pre-treatment sampled in the fall of 2001 was
was similar for all enzymes at each treatment unit. Furthermore, post‐treatment potential enzyme similar for all enzymes at each treatment unit. Furthermore, post-treatment potential enzyme activity
activity not significantly among treatments for all individual at each was alsowas not also significantly
differentdifferent among treatments
for all individual
enzymes enzymes at each sampling
sampling period. The variable that demonstrated the greatest effect on potential enzyme activity was period (Figure 3). The variable that demonstrated the greatest effect on potential enzyme activity was
time, with thinning plus fire exhibiting a higher inter‐annual variation than the other fuel reduction time, with thinning plus fire exhibiting a higher inter-annual variation than the other fuel reduction
treatments or the control (p = 0.001, den. df = 5.864). There was no treatment by time interactions for treatments or the control (p = 0.001, den. df = 5.864). There was no treatment by time interactions for
any of the enzyme activities measured when using natural log transformed data. any of the enzyme activities measured when using natural log transformed data.
Community-level physiological profiles based on C substrate utilization for mineral soil (0–5 cm)
showed no significant differences during pre- or post-treatment sample periods for either bacteria
(Figure 4) or fungi (Figure 5). Neither total plate activity nor normalized C substrate utilization patterns
showed any statistical differences among treatments (p = 0.242, den. df = 6) or treatment ˆ sampling
date interactions (p = 0.671, den. df = 6).
9
Forests 2016, 7, 45
Forests 2016, 7, 45 10 of 19
Figure 3. The observed mean + SE (n = 3) potential activity of eight enzymes in mineral soil (0–5 cm)
preand post-fuel reduction treatments at the Southwestern Plateau, Fire and Fire Surrogate study site.
Figure 3. The observed mean + SE (n = 3) potential activity of eight enzymes in mineral soil (0–5 cm) Error bars are not visible when errors were small.
pre‐ and post‐fuel reduction treatments at the Southwestern Plateau, Fire and Fire Surrogate study site. Error bars are not visible when errors were small. Community‐level physiological profiles based on C substrate utilization for mineral soil (0–5 cm) showed no significant differences during pre‐ or post‐treatment sample periods for either bacteria or fungi (Figure 4). Neither total plate activity nor normalized C substrate utilization patterns showed any statistical differences among treatments (p = 0.242, den. df = 6) or treatment × sampling date interactions (p = 0.671, den. df = 6). Forests 2016, 7, 45 Forests
2016, 7, 45
11 of 19
0.4
0.4
carbohydrates
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0.0
0.4
Control
Fire only
Thinning only
Thinning plus fire
0.4
carbolixic acids
% total absorbance
polymers
amines and amides
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0.0
0.4
0.4
amino acids
miscelaneous
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0.0
pre-treatment
6 months
post-treatment
1 yr posttreatment
pre-treatment
1 yr posttreatment
6 months
post treatment
2 yr posttreatment
2 yr posttreatment
Figure
4. Community-level physiological profiles for bacteria (Biolog™ EcoPlate) in surface mineral
Figure 4. Community‐level physiological profiles for bacteria (Biolog™ EcoPlate) in surface mineral soil soil (0–5 cm) 1‐year post‐treatment at the Southwestern Plateau, Fire and Fire Surrogate study site. (0–5 cm) 1-year post-treatment at the Southwestern Plateau, Fire and Fire Surrogate study site.
DataData shown are the observed mean catabolic activity + SE (n = 3) by substrate guild, normalized to the shown are the observed mean catabolic activity + SE (n = 3) by substrate guild, normalized to
catabolic activity
activity in that treatment.
treatment. No total catabolic
the total
in that
No significant significant differences differences(α (α= =0.05) 0.05)were werefound foundamong among
treatments based on a multi‐response permutation procedure. treatments
based on a multi-response permutation procedure. 11
Forests 2016, 7, 45 Forests
2016, 7, 45
12 of 19
carbohydrate
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0.0
carboxylic acid
amine & amides
0.6
% total absorbance
polymer
Control
Fire only
Thinning only
Thinning plus fire
0.6
0.4
0.4
0.2
0.2
0.0
0.0
amino acids
miscelaneous
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0.0
pre-treatment
6 months
post-treatment
1 yr posttreatment
pre-treatment
2 yr posttreatment
6 months
post-treatment
1 yr posttreatment
2 yr posttreatment
Figure
5. Community-level physiological profiles for fungi (Biolog™ SFN2) in surface mineral soil
Figure 5. Community‐level physiological profiles for fungi (Biolog™ SFN2) in surface mineral soil (0–
(0–5
cm)1‐year 1-yearpost‐treatment post-treatment at at the the Southwestern
site.
Data
5 cm) Southwestern Plateau,
Plateau, Fire
Fire and
and Fire
Fire Surrogate
Surrogate study
study site. Data shown
are observed mean catabolic activity + SE (n = 3) by substrate guild, normalized to the total
shown are observed mean catabolic activity + SE (n = 3) by substrate guild, normalized to the total catabolic
activity in that treatment. No significant differences (α = 0.05) were found among treatments
catabolic activity in that treatment. No significant differences (α = 0.05) were found among treatments based
on a multi-response permutation procedure.
based on a multi‐response permutation procedure. Net N mineralization and nitrification rates were similar statistically among the pre‐treatment Net
N mineralization and nitrification rates were similar statistically among the pre-treatment
and and 2‐year post‐sampling periods (Figure Only the
the 6‐month post‐treatment sampling and
1-1‐ and
2-year
post-sampling
periods
(Figure
6).6). Only
6-month
post-treatment
sampling
demonstrated any statistically significant differences in net N transformation rates among treatments. demonstrated any statistically significant differences in net N transformation rates among treatments.
Six months post‐treatment net N mineralization rates were 3 times higher for the thinning plus fire Six
months post-treatment net N mineralization rates were 3 times higher for the thinning plus fire
treatment than the thinning only treatment (p = 0.003, den. df = 5.995), but not statistically different treatment
than the thinning only treatment (p = 0.003, den. df = 5.995), but not statistically different than
than the control (Figure 6A). Net nitrification rates also exhibited significant effects for the 6‐month the control (Figure 6A). Net nitrification rates also exhibited significant effects for the 6-month samples.
samples. Net nitrification for thinning plus fire was six times greater than the control and was 12
Forests 2016, 7, 45
13 of 19
Forests 2016, 7, 45 Net nitrification
for thinning plus fire was six times greater than the control and was significantly
different than the thinning only and control treatments (Figure 6B). Net nitrification pre-treatment was
significantly different than the thinning only and control treatments (Figure 6B). Net nitrification pre‐
approximately 50% of the net N mineralization for all treatments, yet at 6 months thinning plus fire net
treatment was approximately 50% all treatments, yet at sampling,
6 months nitrification
accounted
for almost
allof ofthe thenet netN N mineralization mineralization.for Other
than the 6-month
thinning plus fire net nitrification accounted for almost all of the net N mineralization. Other than there was no significant treatment by time interactions post-treatment.
the 6‐month sampling, there was no significant treatment by time interactions post‐treatment. Figure 6. Mean ± one standard error (n = 3) net nitrogen (N) mineralization (A) and nitrification rates Figure 6. Mean ˘ one standard error (n = 3) net nitrogen (N) mineralization (A) and nitrification
rates (B) in mineral soil (0–15 cm) at the Southwestern Plateau, Fire and Fire Surrogate study site
(B) in mineral soil (0–15 cm) at the Southwestern Plateau, Fire and Fire Surrogate study site pre‐ and preand post-fuel
reduction
treatments.
Different
letters
a given
sampling
dateindicate indicate significant significant
post‐fuel reduction treatments. Different letters for a forgiven sampling date differences, but sampling dates that share letters are not significantly different (α = 0.05) using
differences, but sampling dates that share letters are not significantly different (α = 0.05) using Tukey‐
Tukey-Kramer
pairwise
comparison
following
a significant
repeated
measures,
generalized
linear
Kramer pairwise comparison following a significant repeated measures, generalized linear mixed mixed
model
analysis
of
treatments.
When
no
letters
are
provided,
mean
values
among
treatments
model analysis of treatments. When no letters are provided, mean values among treatments were were
similar statistically.
similar statistically. 4. Discussion 4. Discussion
Developing fuel reduction strategies that create resistance to future disturbances in fire‐prone Developing fuel reduction strategies that create resistance to future disturbances in fire-prone
forest ecosystems will and instituting
instituting fire forest ecosystems
will require require testing testing a a wide wide range range of of forest forest structures structures [37,71] [37,71] and
fire
protocols that restore soil processes thought to have been altered with fire suppression [21,72]. The protocols
that restore soil processes thought to have been altered with fire suppression [21,72]. The FFS
FFS network study was specifically designed to assess the impact of three alternative fuel reduction network study was specifically designed to assess the impact of three alternative fuel reduction
treatments on a national level. To test these treatments, the FFS experimental design included pre‐
treatments on a national level. To test these treatments, the FFS experimental design included
treatment data for comparison with post‐treatment results repeated over time with true replication of each treatment at each location. Treatment plot size of the FFS treatments was also designed to better simulate operational application of these fuel treatments compared to most previous studies. 13
Forests 2016, 7, 45
14 of 19
pre-treatment data for comparison with post-treatment results repeated over time with true replication
of each treatment at each location. Treatment plot size of the FFS treatments was also designed to
better simulate operational application of these fuel treatments compared to most previous studies.
We specifically tested fuel reduction impacts on N dynamics, soil nutrients, and microbial processes
and composition in ponderosa pine stands of northern Arizona.
Prescribed fire did reduce the mass of the forest floor at SWP in the short-term, yet forest floor
quality (i.e., C:N ratio) did not change following treatments. An unexpected outcome was that the
fire only treatment had no effect on net N mineralization or nitrification, substrate utilization, or
enzyme activity. Previously in southwestern ponderosa pine forests, Kaye and colleagues [6,21] found
that fire alone increased short-term (2-year) net nitrification rates. Significant differences between
SWP and this previous study include a reduction and change in composition of the forest floor
prior to prescribed fire, and the relatively small plot sizes (0.25 ha). To simulate pre-settlement fire
behavior, Kaye and colleagues removed the entire forest floor from the thinning plus fire plots, and
then pine needle litter equivalent to 2–4 year of annual litterfall along with 672 kg¨ha´1 of locally
harvested aboveground native grass and forb clippings were added. Forest floor at the SWP plots
was unaltered prior to burning, and being substantially larger, also exhibited much greater spatial
heterogeneity in fire intensity. Nitrogen transformations for fire only returned to pre-treatment levels
after 1-year, suggesting a short-term effect, as we hypothesized. Several other prescribed fire studies in
ponderosa pine have also shown the impacts of fire on available N are only short-term [20,30,73]. As we
hypothesized, fungi were significantly reduced in the forest floor, but fire had no appreciable effect on
forest floor bacteria or mineral soil fungi and bacteria. Our results support previous studies where both
prescription fire and wildfire decreased fungal dominance within microbial populations [23,27] as fungi
are more susceptible to the heat of fire than bacteria [18]. One concern with using fire in mitigation
treatments is the possible long-term decrease with repeated burning on fungal populations [27].
However, ponderosa pine forests of the southwestern US naturally burned at short-time intervals
pre-settlement. Because we have no microbial measurements in similar forests where a pre-settlement
fire regime has been maintained (i.e., a “reference” forest), it is unclear whether this compositional
change in soil microbial populations observed at the SWP following fire is outside their historic range
in variability in these forests.
Thinning only increased mineral soil total C concentrations even though most of the harvested
residues were removed from the SWP sites, which reduced the forest floor mass post treatment. Mixing
of organic material into the mineral soil by equipment during treatments is the most likely cause for
this increase. We hypothesized that N would be immobilized due to the addition of thinning residues,
but removal of the majority of residues at SWP left the forest floor comparable to the controls. Thinning
only did not alter our microbial populations in either the forest floor or mineral soil, and there was no
effect on microbial activities we measured. Boyle et al. [74] also found little evidence of thinning impacts
on microorganisms in mineral soil from the same study site used by Kaye and colleagues [21]. Microbial
response to thinning in other forest types has been shown to be quite variable, from reduced microbial
biomass without altering the population structure [44], to structural differences without altering
microbial biomass [75–77], to altered biomass and structure [78]. The microbial responses to thinning
are in part quite variable due to differences in thinning intensities, harvest practices, post-treatment
site preparations, and timing of thinning relative to soil measurements, making comparisons among
studies problematic.
Thinning followed by prescribed fire did not increase forest floor mass as we originally thought
due to thinning residues being removed prior to burning. However, different than the thinning
only treatment, we did not observe an increase in mineral soil total C. The thinning plus fire
produced a short-term increase in net N mineralization and nitrification 6-months post-treatment.
Net N mineralization and nitrification were not affected by thinning and burning treatments in the
FFS Network analysis, and there were only minor within-site differences [14]. Our 1- and 2-year
post-treatment samples reflect these same overall FFS Network patterns. Kaye et al. [6] also maintained
Forests 2016, 7, 45
15 of 19
that fire alone produced greater short-term (2-year) net nitrification rates than thinning plus fire.
Similar to the fire only treatment, fungal populations in the forest floor were reduced, yet neither
bacteria in the forest floor or mineral soil populations were affected. Previous research in ponderosa
pine forests has found that soil responses from combining thinning and fire treatments are not simple
additive effects of the individual treatment effects.
Soil responses appear to be resistant to wildfire mitigation treatments under current operational
prescriptions. At SWP, results were consistent with the network scale FFS analysis [79] where C storage,
pH, and extractable base cations were not affected by treatments, but C and base cations were highly
variable between annual samplings, and within and among sites [15,80]. There were two specific sites
in the network-scale FFS analysis that showed increases in pH the first year after thinning and burning,
yet pH returned to pre-treatment levels by the 2-year sampling [79]. The pH increases at these sites
were attributed to higher burn severity. Neither the FFS network, nor the SWP study site specifically,
found significant differences in mineral soil cations [79], and this result is most likely due to the low
intensity often found with prescription fires. Due to high temperature thresholds, cations are not
easily lost in the gaseous form, but often are the major constituents of ash deposited on the mineral
soil surface following fire [18]. The spatially variable nature of prescribed fire can often confound
treatment effects; indeed, fire generally showed small and idiosyncratic effects on soil properties and
processes at the FFS network scale [79]. The FFS results support the conclusion that total C and N in
the mineral soil is relatively insensitive to fuel reduction treatments. Our results at SWP, even with the
additional sampling at 6 months and 2-year, concurred with this network finding. Boerner et al. [15]
did find increased C:N ratios in the mineral soils across the entire FFS network for thinning only,
yet the SWP site, where slash residues were removed from the thinned plots, no effect was found.
A meta-analysis by Johnson and Curtis [80] concluded that changes in total mineral soil C and N
contents in coniferous stands following thinning were the result of harvest method and treatment of
residues. Total C and N contents in the mineral soil have initially been unaffected following restoration
treatments that included thinning and prescribed fire in other ponderosa pine stands in northern
Arizona [6,79], but long-term increases in total soil N have also been reported [31,74]. The limited
effects of the FFS thinning treatments are due in part to modest stand density reductions from a single
application. To date no further treatments have been performed at the SWP site. Treatments to maintain
a forest structure that mitigate stand-replacing wildfires and create resilient forests necessitates repeat
treatments over time.
5. Conclusions
Reducing fuels to decrease intensity and spatial extent of wildfires in ponderosa pine forests
is and will continue to be a major concern for land managers. The SWP implementation of the FFS
study focused on ponderosa pine forests of the southwestern US, a region that has seen the scale and
intensity of wildfires dramatically increase over the last several decades. Overall, soil responses to
fuel reduction treatments at SWP, if they occurred at all, were short-term (<1 year), and support the
conclusions from the multi-site FFS network meta-analysis [79]. Our more intensive sampling results
from the SWP site and the extensive network-wide FFS [79] suggest the mineral soil and associated
microorganisms are resistant to disturbances imposed by these modest fuel reduction treatments in the
short-term. We suspect that a more intense alteration to forest structure with repeated burning could
change microbial community structure and biomass. These changes would have the potential to alter
decomposition and nutrient mineralization processes they mediate [6]. Given the large spatial scale
that fuel treatments are typically applied, continued monitoring of treatments with repeated burning
is essential to determine the longer-term implications to the structure and function of these forests.
Acknowledgments: This is Contribution Number 208 of the National Fire and Fire Surrogate Project (FFS), funded
by the U.S. Joint Fire Science Program. Additional funding was provided by the Rocky Mountain Research Station
(Research Joint Venture 06-JV-11221615-228). We thank Dana Erickson for field and laboratory assistance. We are
Forests 2016, 7, 45
16 of 19
also grateful to Ralph Boerner, Ohio State University, for technical assistance and leadership of the soils group of
the Fire-Fire Surrogate network and Carl Edminster (RMRS), the Southwestern Plateau FFS site manager.
Author Contributions: Both authors involved provided considerable input during the planning and
implementation of this research. Steven T. Overby directed the field sampling, laboratory analysis, statistical
analysis, and drafted the manuscript. Stephen C. Hart provided essential comments that greatly improved
manuscript quality.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
Covington, W.W.; Fulé, P.; Moore, M.M.; Hart, S.C.; Kolb, T.; Mast, J.; Sackett, S.S.; Wagner, M. Restoring
ecosystem health in ponderosa pine forests of the Southwest. J. For. 1997, 95, 23–29.
Fulé, P.Z.; Covington, W.W.; Moore, M.M. Determining reference conditions for ecosystem management of
Southwestern ponderosa pine forests. Ecol. Appl. 1997, 7, 895–908. [CrossRef]
White, A.S. Presettlement regeneration patterns in a Southwestern ponderosa pine stand. Ecology 1985,
66, 589–594. [CrossRef]
Swetnam, T.W.; Baisan, C.H. Historical fire regime patterns in the Southwestern United States since AD
1700. Fire Effects in Southwestern Forests; Allen, C.D., Ed.; U.S. Department of Agriculture, Forest Service: Fort
Collins, CO, USA, 1996; pp. 11–32.
O’Brian, R.A. Arizona’s Forest Resources, 1999; U.S. Department of Agriculture, Forest Service: Ogden, UT,
USA, 2002.
Kaye, J.P.; Hart, S.C.; Fulé, P.Z.; Covington, W.W.; Moore, M.M.; Kaye, M.W. Initial carbon, nitrogen,
and phosphorus fluxes following ponderosa pine restoration treatments. Ecol. Appl. 2005, 15, 1581–1593.
[CrossRef]
Bakker, J.D.; Moore, M.M. Controls on vegetation structure in Southwestern ponderosa pine forests, 1941
and 2004. Ecology 2007, 88, 2305–2319. [CrossRef] [PubMed]
Pearson, G.A. A twenty-year record of changes in an Arizona ponderosa pine forest. Ecology 1933, 14, 272–285.
[CrossRef]
Savage, M.A.; Brown, P.M.; Feddema, J. The role of climate in a pine forest regeneration pulse in the
southwestern United States. Ecoscience 1996, 3, 310–318.
Covington, W.W.; Moore, M.M. Southwestern ponderosa forest structure. Changes since Euro-American
settlement. J. For. 1994, 92, 39–47.
Mast, J.N.; Fulé, P.Z.; Moore, M.M.; Covington, W.W.; Waltz, A.E.M. Restoration of presettlement age
structure of an Arizona ponderosa pine forest. Ecol. Appl. 1999, 9, 228–239. [CrossRef]
Pollet, J.; Omi, P.N. Effect of thinning and prescribed burning on crown fire severity in ponderosa pine
forests. Int. J. Wildland Fire 2002, 11, 1–10. [CrossRef]
Strom, R.A.; Fulé, P.Z. Pre-wildfire fuel treatments affect long-term ponderosa pine forest dynamics. Int. J.
Wildland Fire 2007, 16, 128–138. [CrossRef]
Boerner, R.E.J.; Giai, C.; Huang, J.; Miesel, J.R. Initial effect of fire and mechanical thinning on soil enzyme
activity and nitrogen transformations in eight North American forest ecosystems. Soil Biol. Biochem. 2008,
40, 3076–3085. [CrossRef]
Boerner, R.E.J.; Huang, J.; Hart, S.C. Fire, thinning, and the carbon economy, effects of the FFS treatments on
carbon storage and sequestration rate. For. Ecol. Manag. 2008, 255, 3081–3097. [CrossRef]
Agee, J.K.; Skinner, C.N. Basic principles of forest fuel reduction treatments. For. Ecol. Manag. 2005,
211, 83–96. [CrossRef]
Kobziar, L.N.; McBride, J.R.; Stephens, S.L. The efficacy of fire and fuels reduction treatments in a Sierra
Nevada pine plantation. Int. J. Wildland Fire 2009, 18, 791–801. [CrossRef]
DeBano, L.F.; Neary, D.G.; Ffolliott, P.F. Fire’s Effects on Ecosystems; John Wiley & Sons: New York, NY, USA,
1998; p. 333.
Bardgett, R.D. The Biology of Soil, A Community and Ecosystem Approach; Oxford University Press: Oxford,
UK, 2005.
Covington, W.W.; Sackett, S.S. Soil mineral nitrogen changes following prescribed burning in ponderosa
pine. For. Ecol. Manag. 1992, 54, 18–191. [CrossRef]
Forests 2016, 7, 45
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
17 of 19
Kaye, J.P.; SHart, C. Ecological restoration alters nitrogen transformations in a ponderosa pine-bunchgrass
ecosystem. Ecol. Appl. 1998, 8, 1052–1060.
Frey, S.D.; Knorr, M.; Parrent, J.L.; Simpson, R.T. Chronic nitrogen enrichment affects the structure and
function of the soil microbial community in temperate hardwood and pine forests. For. Ecol. Manag. 2004,
196, 159–171. [CrossRef]
Hart, S.C.; DeLuca, T.H.; Newman, G.S.; MacKenzie, D.M.; Boyle, S.I. Post-fire vegetative dynamics as
drivers of microbial community structure and function in forest soils. For. Ecol. Manag. 2005, 220, 166–184.
[CrossRef]
Simonin, K.; TKolb, E.; Montes-Helu, M.; Koch, G.W. The influence of thinning on components of stand
water balance in a ponderosa pine forest stand during and after extreme drought. Agric. For. Meteorol. 2007,
143, 266–276. [CrossRef]
Pietikäinen, J.; Fritze, H. Clear-cutting and prescribed burning in coniferous forest, comparison of effects
on soil fungal and total microbial biomass, respiration activity and nitrification. Soil Biol. Biochem. 1995,
27, 101–109. [CrossRef]
Pietikäinen, J.; Kiikkilä, O.; Fritze, H. Charcoal as a habitat for microbes and its effect on the microbial
community underlying humus. Oikos 2000, 89, 231–242. [CrossRef]
Cairney, J.W.G.; Bastias, B.A. Influences of fire on forest soil fungal communities. Can. J. For. Res. 2007,
37, 207–215. [CrossRef]
Waldrop, M.P.; McColl, J.G.; Powers, R.F. Effects of forest postharvest management practices on enzyme
activities in decomposing litter. Soil Sci. Soc. Am. J. 2003, 67, 1250–1256. [CrossRef]
Grayston, S.J.; Renneberg, H. Assessing effects of forest management on microbial community structure in
a central European beech forest. Can. J. For. Res. 2006, 40, 1901–1907. [CrossRef]
Kaye, J.P.; Hart, S.C. Restoration and canopy-type effects on soil respiration in a ponderosa pine-bunchgrass
ecosystem. Soil Sci. Soc. Am. J. 1998, 62, 1062–1072. [CrossRef]
Grady, K.C.; Hart, S.C. Influences of thinning, prescribed burning, and wildfire on soil processes and
properties in southwestern ponderosa pine forests, A retrospective study. For. Ecol. Manag. 2006, 234, 123–135.
[CrossRef]
DeLuca, T.H.; Zouhar, K.L. Effects of selection harvest and prescribed fire on the soil nitrogen status of
ponderosa pine forests. For. Ecol. Manag. 2000, 138, 263–271. [CrossRef]
Certini, G. Effects of fire on properties of forest soils, a review. Oecologia 2005, 143, 1–10. [CrossRef] [PubMed]
McIver, J.D.; Erickson, K.; Youngblood, A. Principal Short-Term Finding of the National Fire and Fire Surrogate
Study; U.S. Department of Agriculture, Forest Service: Portland, OR, USA, 2012.
Stephens, S.L.; Moghaddas, J.J.; Edminster, C.; Fiedler, C.E.; Haase, S.; Harrington, M.; Keeley, J.E.;
Knapp, E.E.; McIver, J.D.; Metlen, K.; et al. Fire treatment effects on vegetation structure, fuels, and
potential fire severity in western U.S. forest. Ecol. Appl. 2009, 19, 305–320. [CrossRef] [PubMed]
McIver, J.D.; Weatherspoon, P.; Edminster, C. A Long-Term Study on the Effects of Alternative Ponderosa Pine
Restoration Treatments. Ponderosa Pine Ecosystems Restoration and Conservation, Steps Toward Stewardship;
U.S. Department of Agriculture, Forest Service: Ogden, UT, USA, 2001.
Stephens, S.L.; McIver, J.D.; Boerner, R.E.J.; Fettig, C.J.; Fontaine, J.B.; Hartsough, B.R.; Kennedy, P.;
Schwilk, D.W. Effects of forest fuel reduction treatments in the United States. BioScience 2012, 62, 549–560.
Faiella, S.M.; Bailey, J.D. Fluctuations in fuel moisture across restoration treatments in semi-arid ponderosa
pine forests of northern Arizona, USA. Int. J. Wildland Fire 2007, 16, 119–127. [CrossRef]
U.S. Department of Agriculture. Terrestrial Ecosystem Survey of the Coconino National Forest; U.S. Department
of Agriculture, Forest Service, Southwestern Region: Albuquerque, NM, USA, 1995.
U.S. Department of Agriculture. Terrestrial Ecosystem Survey of the Kaibab National Forest; U.S. Department of
Agriculture, Forest Service, Southwestern Region: Albuquerque, NM, USA, 1991.
Schwilk, D.W.; Keeley, J.E.; Knapp, E.E.; McIver, J.; Bailey, J.D.; Fettig, C.J.; Fiedler, C.E.; Harrod, R.J.;
Moghaddas, J.J.; Outcalt, K.W.; et al. The national Fire and Fire Surrogate study, effects of fuel reduction
methods on forest vegetation structure and fuels. Ecol. Appl. 2009, 19, 285–304. [CrossRef] [PubMed]
Youngblood, A. Thinning and burning in dry coniferous forests of the western United State, effectiveness in
altering diameter distributions. For. Sci. 2010, 56, 46–59.
Forests 2016, 7, 45
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
18 of 19
Hendershot, W.H.; Lalande, H.; Duquette, M. Soil reaction and exchangeable acidity. In Soil Sampling and
Methods of Analysis; Carter, M.R., Ed.; Canadian Society of Soil Science, Lewis Publishers: Boca Raton, FL,
USA, 1993; pp. 141–145.
Hassett, J.E.; Zak, D.R. Aspen harvest intensity decreases microbial biomass, extracellular enzyme activity,
and soil nitrogen cycling. Soil Sci. Soc. Am. J. 2005, 69, 227–235. [CrossRef]
Leckie, S. Methods of microbial community profiling and their application to forest soils. For. Ecol. Manag.
2005, 220, 88–106. [CrossRef]
Frostegard, A.; Bååth, E. The use of phospholipid fatty acid analysis to estimate bacterial and fungal biomass
in soil. Biol. Fertil. Soils 1996, 22, 59–65. [CrossRef]
Zelles, L. Fatty acid patterns of phospholipids and lipopolysaccharides in the characterization of microbial
communities in soil, A review. Biol. Fertil. Soils 1999, 29, 111–129. [CrossRef]
White, D.C.; Davis, W.M.; Nickels, J.S.; King, J.C.; Bobbie, R.J. Determination of the sedimentary microbial
biomass by extractable lipid phosphate. Oecologia 1979, 40, 51–62. [CrossRef]
Frostegard, A.; Tunlid, A.; Bååth, E. Microbial biomass measured as total lipid phosphate in soils of different
organic contents. J. Microbiol. Methods 1991, 14, 151–163. [CrossRef]
Schweitzer, J.A.; Bailey, J.K.; Fischer, D.G.; LeRoy, C.J.; Lonsdorf, E.V.; Whitham, T.G.; Hart, S.C.
Soil microorganism—Plant interactions; heritable relationship between plant genotype and associated
microorganisms. Ecology 2008, 89, 773–781. [CrossRef] [PubMed]
Eivazi, F.; Tabatabai, M.A. Phosphatases in soils. Soil Biol. Biochem. 1977, 9, 162–172. [CrossRef]
Eivazi, F.; Tabatabai, M.A. Glucosidases and galactosidases in soils. Soil Biol. Biochem. 1988, 20, 601–606.
[CrossRef]
Sinsabaugh, R.L. Enzymatic analysis of microbial pattern and process. Biol. Fertil. Soils 1994, 17, 69–74.
[CrossRef]
Eivazi, F.; Bayan, M. Effects of long-term prescribed burning on the activity of select soil enzymes in
an oak-hickory forest. Can. J. For. Res. 1996, 26, 1799–1804. [CrossRef]
Olander, L.P.; Vitousek, P.M. Regulation of soil phosphatase and chitinase activity by N and P availability.
Biogeochemistry 2000, 49, 18–191. [CrossRef]
Ganeshamurthy, A.N.; Nielsen, N.E. Arylsulfatase and the biochemical mineralization of soil organic sulfur.
Soil Biol. Biochem. 1990, 22, 1163–1165. [CrossRef]
Sinsabaugh, R.L.; Antibus, R.K.; Linkins, A.E. An enzymatic approach to the analysis of microbial activity
during plant litter decomposition. Agric. Ecosyst. Environ. 1991, 34, 43–54. [CrossRef]
Classen, A.T.; Boyle, S.I.; Haskins, K.; Overby, S.; Hart, S.C. Community-level physiological
profiles of bacteria and fungi, plate type and incubation temperature influences on contrasting soils.
FEMS Microbiol. Ecol. 2003, 44, 319–328. [CrossRef]
Garland, J.; Mills, A. Classification and characterization of heterotrophic microbial communities on the basis
of patterns of community-level sole-carbon-source-utilization. Appl. Environ. Microbiol. 1991, 57, 2351–2359.
[PubMed]
Kennedy, A.C. Carbon utilization and fatty acid profiles for characterization of bacteria. In Methods of
Soil Analysis, Part 2 Microbiological and Biochemical Properties; SSSA Book Series No. 5; Weaver, R.W.,
Mickelson, S.H., Eds.; Soil Science Society of America: Madison, WI, USA, 1994; pp. 548–551.
Dobranic, J.K.; Zak, J.C. A microtiter plate procedure for evaluating functional diversity. Mycologia 1999,
91, 756–765. [CrossRef]
Hart, S.C.; Stark, J.M.; Davidson, E.A.; Firestone, M.K. Nitrogen mineralization, immobilization, and
nitrification. In Methods of Soil Analysis, Part 2 Microbiological and Biochemical Properties; SSSA Book Series
No. 5; Weaver, R.W., Mickelson, S.H., Eds.; Soil Science Society of America: Madison, WI, USA, 1994;
pp. 985–1018.
Lachat Instruments, Inc. QuickChem Method No. 12-107-06-1-B; Lachat Instruments, Inc.: Milwaukee, WI,
USA, 2001.
Lachat Instruments, Inc. QuickChem Method No. 10-107-04-1-C; Lachat Instruments, Inc.: Milwaukee, WI,
USA, 2000.
Schabenberger, O. Introducing the GLIMMIX procedure for generalized linear mixed models. In Proceedings
of the Thirtieth Annual SAS®Users Group International Conference, Cary, NC, USA, 26–29 April 2005.
Forests 2016, 7, 45
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
19 of 19
Kenward, M.G.; Roger, J.H. Small Sample Inference for Fixed Effects from Restricted Maximum Likelihood.
Biometrics 1997, 53, 983–997. [CrossRef] [PubMed]
Bolker, B.M.; Brooks, M.E.; Clark, C.J.; Geange, S.W.; Poulsen, J.R.; Stevens, M.H.H.; White, J.S. Generalized
linear mixed models, a practical guide for ecology and evolution. Trend Ecol. Evol. 2008, 2, 127–135.
[CrossRef] [PubMed]
Mielke, P.W., Jr.; Berry, K.J. Permutation Methods, A Distance Function Approach; Springer-Verlag: New York,
NY, USA, 2001.
McCune, B.; Grace, J.B. Analysis of Ecological Communities; MjM Software Design: Gleneden Beach, OR,
USA, 2002.
Petrondas, D.A.; Gabriel, K.R. Multiple comparisons by rerandomization tests. J. Am. Stat. Assoc. 1983,
78, 949–957. [CrossRef]
Milar, L.I.; Stephenson, N.L.; Stephens, S.L. Climate change and forests of the future, managing in the face of
uncertainty. Ecol. Appl. 2007, 17, 2145–2151. [CrossRef]
DeLuca, T.H.; Sala, A. Frequent fire alters nitrogen transformations in ponderosa pine stands of the Inland
Northwest. Ecology 2006, 87, 2511–2522. [CrossRef]
Wright, R.J.; Hart, S.C. Nitrogen and phosphorus status in a ponderosa pine forest after 20 years of interval
burning. Ecoscience 1997, 4, 526–533.
Boyle, S.I.; Hart, S.C.; Kaye, J.P.; Waldrop, M.P. Restoration and canopy type influence soil microflora in
a ponderosa pine forest. Soil Sci. Soc. Am. J. 2005, 69, 1627–1638. [CrossRef]
Maassen, S.; Fritze, F.; Wirth, S. Response of soil microbial biomass, activities, and community structure at
a pine stand in northeastern Germany 5 years after thinning. Can. J. For. Res. 2006, 36, 1427–1434. [CrossRef]
Siira-Pietikäinen, A.; Pietikäinen, J.; Fritze, H.; Haimi, J. Short-term responses of soil decomposer
communities to forest management, clear felling versus alternative forest harvesting methods. Can. J.
For. Res. 2001, 31, 88–99. [CrossRef]
Hannam, K.D.; Quideau, S.A.; Kishcuk, B.E. Forest floor microbial communities in relation to stand
composition and timber harvesting northern Alberta. Soil Biol. Biochem. 2006, 38, 2565–2575. [CrossRef]
Pietikäinen, J.; Tikka, P.J.; Valkonen, S.; Isomäki, A.; Fritze, H. Is the soil microbial community related to the
basal area of trees in a Scots pine stand? Soil Biol. Biochem. 2007, 39, 1832–1834. [CrossRef]
Boerner, R.E.J.; Huang, J.; Hart, S.C. Impacts of fire and fire surrogate treatments on forest soil properties,
a meta-analytical approach. Ecol. Appl. 2009, 19, 338–358. [CrossRef] [PubMed]
Johnson, D.W.; Curtis, P.S. Effects of forest management on soil C and N storage, meta-analysis.
For. Ecol. Manag. 2001, 140, 227–238. [CrossRef]
© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons by Attribution
(CC-BY) license (http://creativecommons.org/licenses/by/4.0/).